CEP3: Community Event Prediction with Neural Point Process on Graph
- URL: http://arxiv.org/abs/2205.10624v1
- Date: Sat, 21 May 2022 15:30:25 GMT
- Title: CEP3: Community Event Prediction with Neural Point Process on Graph
- Authors: Xuhong Wang, Sirui Chen, Yixuan He, Minjie Wang, Quan Gan, Yupu Yang,
Junchi Yan
- Abstract summary: We propose a novel model combining Graph Neural Networks and Marked Temporal Point Process (MTPP)
Our experiments demonstrate the superior performance of our model in terms of both model accuracy and training efficiency.
- Score: 59.434777403325604
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Many real world applications can be formulated as event forecasting on
Continuous Time Dynamic Graphs (CTDGs) where the occurrence of a timed event
between two entities is represented as an edge along with its occurrence
timestamp in the graphs.However, most previous works approach the problem in
compromised settings, either formulating it as a link prediction task on the
graph given the event time or a time prediction problem given which event will
happen next. In this paper, we propose a novel model combining Graph Neural
Networks and Marked Temporal Point Process (MTPP) that jointly forecasts
multiple link events and their timestamps on communities over a CTDG. Moreover,
to scale our model to large graphs, we factorize the jointly event prediction
problem into three easier conditional probability modeling problems.To evaluate
the effectiveness of our model and the rationale behind such a decomposition,
we establish a set of benchmarks and evaluation metrics for this event
forecasting task. Our experiments demonstrate the superior performance of our
model in terms of both model accuracy and training efficiency.
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